Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Dingliwal, Saket"'
Large language models (LLMs) can generate fluent summaries across domains using prompting techniques, reducing the need to train models for summarization applications. However, crafting effective prompts that guide LLMs to generate summaries with the
Externí odkaz:
http://arxiv.org/abs/2410.02741
Automatic Speech Recognition (ASR) traditionally assumes known domains, but adding data from a new domain raises concerns about computational inefficiencies linked to retraining models on both existing and new domains. Fine-tuning solely on new domai
Externí odkaz:
http://arxiv.org/abs/2406.17935
Autor:
Peri, Raghuveer, Jayanthi, Sai Muralidhar, Ronanki, Srikanth, Bhatia, Anshu, Mundnich, Karel, Dingliwal, Saket, Das, Nilaksh, Hou, Zejiang, Huybrechts, Goeric, Vishnubhotla, Srikanth, Garcia-Romero, Daniel, Srinivasan, Sundararajan, Han, Kyu J, Kirchhoff, Katrin
Integrated Speech and Large Language Models (SLMs) that can follow speech instructions and generate relevant text responses have gained popularity lately. However, the safety and robustness of these models remains largely unclear. In this work, we in
Externí odkaz:
http://arxiv.org/abs/2405.08317
Autor:
Das, Nilaksh, Dingliwal, Saket, Ronanki, Srikanth, Paturi, Rohit, Huang, Zhaocheng, Mathur, Prashant, Yuan, Jie, Bekal, Dhanush, Niu, Xing, Jayanthi, Sai Muralidhar, Li, Xilai, Mundnich, Karel, Sunkara, Monica, Srinivasan, Sundararajan, Han, Kyu J, Kirchhoff, Katrin
Large language models (LLMs) have shown incredible proficiency in performing tasks that require semantic understanding of natural language instructions. Recently, many works have further expanded this capability to perceive multimodal audio and text
Externí odkaz:
http://arxiv.org/abs/2405.08295
Autor:
Jayanthi, Sai Muralidhar, Kulshreshtha, Devang, Dingliwal, Saket, Ronanki, Srikanth, Bodapati, Sravan
Personalization of automatic speech recognition (ASR) models is a widely studied topic because of its many practical applications. Most recently, attention-based contextual biasing techniques are used to improve the recognition of rare words and doma
Externí odkaz:
http://arxiv.org/abs/2311.08402
Connectionist Temporal Classification (CTC) models are popular for their balance between speed and performance for Automatic Speech Recognition (ASR). However, these CTC models still struggle in other areas, such as personalization towards custom wor
Externí odkaz:
http://arxiv.org/abs/2307.00759
Autor:
Bhatia, Anshu, Sinha, Sanchit, Dingliwal, Saket, Gopalakrishnan, Karthik, Bodapati, Sravan, Kirchhoff, Katrin
Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks. However, such representations may be skewed toward canonical data characteristics of suc
Externí odkaz:
http://arxiv.org/abs/2307.00453
Autor:
Bansal, Hritik, Gopalakrishnan, Karthik, Dingliwal, Saket, Bodapati, Sravan, Kirchhoff, Katrin, Roth, Dan
Language models have been shown to perform better with an increase in scale on a wide variety of tasks via the in-context learning paradigm. In this paper, we investigate the hypothesis that the ability of a large language model to in-context learn-p
Externí odkaz:
http://arxiv.org/abs/2212.09095
Autor:
Dingliwal, Saket, Sunkara, Monica, Bodapati, Sravan, Ronanki, Srikanth, Farris, Jeff, Kirchhoff, Katrin
End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently. In these models, a non-autoregressive CTC decoder is often used at inference time due to its speed an
Externí odkaz:
http://arxiv.org/abs/2210.09510
Autor:
Dingliwal, Saket, Shenoy, Ashish, Bodapati, Sravan, Gandhe, Ankur, Gadde, Ravi Teja, Kirchhoff, Katrin
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead. In this work, we introduce domain-prompts,
Externí odkaz:
http://arxiv.org/abs/2112.08718